Custom data aggregation and integration processing

ABSTRACT

Agents are configured to collect data from terminals and send the data in real time to a data integrator. The data integrator identifies the data types and processes custom aggregation on select ones of data types from the data. The data and the output of any aggregations are translated to a select output format and sent to one or more select resources for further processing. The data integrator determines when to send the data and the output from any aggregation in the select output format to the one more select resources based on a defined condition. In an embodiment, the data integrator sends the data and output from any aggregation in real time to at least one of the one or more select resources.

BACKGROUND

Enterprises collect a variety of data about their business. Some of thisdata is collected in batch modes while much of the data is collected inreal time. The sources for the data include enterprise-based devices,web devices, mobile devices, etc. Often, the data collected is processedfor driving support and maintenance decisions, planning decisions,inventorying decisions, staffing decisions, interacting with customerdecisions, and the like.

One problem associated with data consumption is that the enterprise iscontinually changing the manners in which the enterprise: wants tocombine portions of the data; wants the data presented; and wants thetiming of the data reported. Another problem is that the enterprise isregularly changing the software services that the enterprise wants toevaluate or to consume the data for decision making within theenterprise. Furthermore, the software services that evaluate or consumeportions of the data are continually changing, being replaced, beingupdated, and being added to the enterprise environment.

In fact, data is plentiful within enterprise environments and in manyways too plentiful. However, information that is derived from the dataand timely reporting of that information to the appropriate consumingservice for enterprise decision making are largely inadequate orineffective.

Each enterprise understands its data and knows how it wants to consumeinformation derived from the data. However, available data interfacesand data services are not customized to any large degree to specificenterprises, which means enterprises are continually lobbying vendorsfor what is needed or switching to new vendors in the hopes that the newvendors provide a closer or better to solution for what the enterprisesbelieves they need.

SUMMARY

In various embodiments, methods and a system are provided for customdata aggregation and integration processing are provided.

According to an embodiment, a method for custom data aggregation andintegration processing is provided. Specially, data that is collected bya plurality of agents from a plurality of terminals is received in realtime. A portion of the data is selectively aggregated into aggregateddata and the aggregated data is translated into a target data format.The aggregated data is provided to a resource at a time when anevaluated condition evaluates to true.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a diagram illustrating components of a custom dataaggregation and integration processing system, according to an exampleembodiment.

FIG. 1B illustrates another system for custom data aggregation andintegration processing, according to an example embodiment.

FIG. 2 is a diagram of a method for custom data aggregation andintegration processing, according to an example embodiment.

FIG. 3 is a diagram of another method for custom data aggregation andintegration processing, according to an example embodiment.

FIG. 4 is a diagram of yet another system for custom data aggregationand integration processing, according to an example embodiment.

DETAILED DESCRIPTION

FIG. 1A is a diagram illustrating components of a custom dataaggregation and integration processing stem 100, according to an exampleembodiment. It is to be noted that the SST configuration managementsystem 100 is shown schematically in greatly simplified form, with onlythose components relevant to understanding of the embodiments beingillustrated.

Furthermore, the various components (that are identified in the FIG. 1A)are illustrated and the arrangement of the components is presented forpurposes of illustration only. It is to be noted that other arrangementswith more or less components are possible without departing from theteachings of custom data aggregation and integration processing,presented herein and below.

Furthermore, the techniques and the systems presented herein and below(for custom data aggregation and integration processing) may include allor some combination of the components shown with the system 100. Themethods are programmed as executable instructions in memory and/ornon-transitory computer-readable storage media and executed on one ormore processors associated with the components/devices.

Specifically, the system 100 includes multiple SCO (Self-CheckoutService) terminals 110, 120, 130, and 140; a SS (Self-Service)management terminal 160, and a Local Area Network (LAN) and/or WAN 150.SCO terminals 110, 120, 130, and 140 include a management interface 105,125, 135, and 145, respectively. The SS management terminal 160 includesa data manager 165. Optionally, the system 100 includes a Wide-AreaNetwork (WAN) 170, a remote server 180 having an instance of the datamanager 185, and/or one or more external services 190.

Initially, it is to be noted that a SCO terminal (110, 120, 130, or 140)is one type of Self-Service Terminal (SST), other types of SSTs that canbe equally utilized with the teachings presented herein include, but arenot limited to, an Automated Teller Machine (ATM), a digital sign, and akiosk.

The management interfaces (105, 125, 135, and 145) are software agentsthat are executed as executable instructions from non-transitory memoryor storage by processors of the SCO terminals (110, 120, 130, and 140).Each instance of the management interface (105, 125, 135, and 145) isresponsible for communicating status, diagnostics, and transactioninformation to its assigned management terminal 160.

In an embodiment, the management interfaces (105, 125, 135, and 145)monitor logs of the SCO terminals (110, 120, 130, and 140) for events,transaction metrics, failures (in software, peripherals, SCO terminals,etc.), etc.

In an embodiment, the management interfaces (105, 125, 135, and 145)register and receive event information from the Operating System (OS) ofthe SCO terminals (110, 120, 130, and 140). The events can be related totransaction metrics, failures, etc.

In an embodiment, the management interfaces (105, 125, 135, and 145) areconfigured to receiving configuration and state information for theirrespective SCO terminals (110, 120, 130, and 140). This can includeterminal status, mode of operation, health, intervention noted of theterminals (110, 120, 130, and 140), and sales transaction information.

In an embodiment, the transaction software executing on the terminals(110, 120, 130, and 140) is modified to record information in a log,such as an eXtensbile Markup Language (XML) file on the terminals (110,120, 130, and 140), which is accessible and read in real time by themanagement interfaces (105, 125, 135, and 145).

In an embodiment, the management interfaces (105, 125, 135, and 145) areconfigured to monitor output produced by transaction software thatexecutes on the SCO terminals (110, 120, 130, and 140), such as throughmonitoring of communication ports being used by the transaction softwareto report output.

The management interfaces (105, 125, 135, and 145) also are configuredto access the LAN and/or WAN 150 and report in real time the collectedevents, metrics, health, intervention, and sales information.

In an embodiment, the management interfaces (105, 125, 135, and 145) canbe custom-configured through an XML file that defines the types of dataand format of data that the management interfaces (105, 125, 135, and145) report in real time over the LAN and/or WAN 150 to the data manager165 of the management terminal 160.

In an embodiment, the management interfaces (105, 125, 135, and 145)report the collected events, metrics, health, intervention, and salesinformation through the LAN 150 (when 150 is a LAN) over a WAN 170 tothe data manager 185 of the remote server 180.

The real-time collected events, metrics, health, intervention, and salesinformation is reported in real time over network connections (150and/or 170) to at least one instance of the data manager 165 and/or 185.

It is to be noted that each of the above-mentioned embodiments are notintended to be mutually exclusive from one another, such that oneinstance of the management interface, such as 110, can process adifferent embodiment from that which is processed by another instance ofthe management interface, such as 120.

The SS management terminal 160 may include real-time management softwarethat permits an operator to view transactions taking place on the SCOterminals (110, 120, 130, and/or 140). The SS management terminal 160may also include reporting software that reports information receivedfrom the management interfaces (105, 125, 135, and 145).

The SS management terminal 160 includes the data manager 165.Alternatively, the SS management terminal 160 includes an agent thatforwards the real-time received terminal events, metrics, health,intervention, and sales information over the WAN 170 to a remoteprocessing instance of the data manager 185 on the remote server 180.

The data manager (165 and 185) is configured to process the receivedterminal events, metrics, health, intervention, and sales information incustom-defined manners defined by a particular enterprise. Thisconfiguration information can be read in real time from a schema.

The schema defines a variety of information, such as data type andformat, aggregation algorithms for processing against defined datatypes, output data types, output data format for any output produced bya specific aggregation algorithm, resource identifier for a resourcethat the data associated with the output data types and aggregationoutputs are to be sent, and a timing attribute indicating when theresource is to receive the data and aggregation outputs. The schema mayalso include custom threshold values for specific aggregation outputsthat alter the timing attribute to a threshold-based reporting approach.It is also noted that multiple resources may receive the data andaggregation outputs through a list of resource identifiers. In anembodiment, the output format may be identified in the specification asa reference to a custom-plugin that converts the data and aggregationoutputs into a needed format for the resource that the data andaggregation outputs are to be sent from the data manager (165 and 185).

The aggregation algorithms can be custom-defined to work on a specificdata type or on a set of data types. In an embodiment, at least oneaggregation algorithm provides a counting mechanism that counts events,metrics, health, intervention, and sales information over a configuredtime period.

The enterprise associated with the SCO terminals (110, 120, 130, and140) may access a user interface to the management terminal 160 todefine the data types, any plugins, any specific aggregation algorithms,timing conditions, receiving resource identifiers, and threshold values.The user interface includes a backend processing component thatautomatically generates the schema. The data manager (165 and 185)processes the schema dynamically to self-configure and initiate itselffor receiving the events, metrics, health, intervention, and salesinformation from the SCO terminals (110, 120, 130, and 140), performingthe custom data aggregations, threshold condition comparisons, customtiming of reporting, and reporting in specific formats to specificresources.

It is noted that the schema-based approach is but one approach that canbe taken and is not intended to limit aspects of the invention presentedherein for custom data aggregation and integration processing.

A “resource” can be: 1) a remote software service, such as one of theexternal services 190; 2) a local software service (local within the LAN150 environment) or remote software service (remotely accessible over aWAN 150); 3) an email address (a specific user or group of users), 4) aphone number (for texting or paging), 5) a storage location (file,directory, etc.), and 6) any other entity that can house or process thedata and aggregation outputs reported by the data manager (165 and 185).

In an embodiment, the real time events, metrics, health, intervention,and sales information received from the SCO terminals (110, 120, 130,and 140) and/or the aggregation outputs can also be streamed in realtime from the data manager (165 and 185) to resources.

In an embodiment, the resource is a dashboard service that processes onthe management terminal 160.

In an embodiment, the health information includes heartbeats, mode ofoperation, and processing state change information for the SCO terminals(110, 120, 130, and 140).

In an embodiment, the events, metrics, health, intervention, and salesinformation can be processed in different manners producing differentdata and aggregation outputs for different resources by the data manager(165 and 185). In this way a single source of input data collected bythe data manager (165 and 185) from a plurality of management interfaces(105, 125, 135, and 145) can result in different data and aggregationoutputs sent to multiple different resources.

In an embodiment, the events, metrics, health, intervention, and salesinformation can be processed in a same manner producing the data andaggregation outputs for multiple different resources by the data manager(165 and 185).

In an embodiment, one or more of the SCO terminals (110, 120, 130, and140) capable in operating in a Self-Service (SS) mode during which acustomer performs a self-checkout and an assisted-checkout mode duringwhich a cashier assisted the customer in checking out. In this manner,the SCO terminals (110, 120, 130, and 140) can be Point-Of-Sale (POS)terminals or SSTs depending upon their mode of operation. The mode ofoperation is also provided by the management interfaces (105, 125, 135,and 145) to the data manager (165 and 185) as part of the healthinformation.

It is to be noted that the terminals (110, 120, 130, and 140) can be allor some combination of mobile-based terminals that execute aSelf-Service Checkout Application to perform transactions. In such acase, the terminals (110, 120, 130, and 140) can communication with thedata manager (165 and 185) over a LAN 150 or a WAN 150, using Wi-Fiand/or cellular communications. In an embodiment, the mobile-basedterminals can include: phones, tablets, laptops, wearable processingdevices, and the like.

An example scenario for the system 100 is now presented with referenceto the FIG. 1B.

FIG. 1B illustrates another system 190 for custom data aggregation andintegration processing, according to an example embodiment.

The system 190 is configured for operation within a LAN environmentwithin a retail store. The system 190 includes a plurality Self ServiceCheckout (SSCO) terminals (corresponding to the SCO terminals (110, 120,130, and 140 of the FIG. 1A). Each SSCO terminal having a Self-checkoutapplication base (corresponding to the discussed transaction manager inthe FIG. 1A), a Remote Console Manager (RCM and corresponding to themanagement interfaces (105, 125, 135, and 145) of the FIG. 1A), a UserDatagram Protocol (UDP) messaging mechanism, and a failed transactionrate calculator. The system 190 further includes a Remote AttendantProgram (RAP) terminal (corresponding to the management terminal 160 ofthe FIG. 1A). The RAP terminal including a RAP application for receivingthe events, metrics, health, intervention, and sales information fromthe SSCO terminals, a UDP messaging mechanism, and a data pump serverapplication (all of which corresponded to the data manager (165 and 185)from the FIG. 1A).

The SSCO terminals sends real-time status information, workflowinformation, and transaction count data to the data pump application.The RCMs detects failures, normal versus abnormal restarts for the SSCOterminals by analyzing the logged data at the SSCO terminals. The RCMssends the data to the data pump application.

The data pump server application listens for real-time data beingreported from the SSCO terminals, the data is made available to pluginmodules a publish/subscribe model; each plugin being configurable as tospecific data types to be routed to it. One such plugin is a dashboardapplication that subscribes for the real-time terminal availabilitydata. A data pump plugin summarizes the data/counts for each SSCO andRAP terminal and for the SSCO terminals as a whole and exports to theretailer's dashboard system.

Real-time data is fed to the data pump application using UDP, TCP, HTTP,message queueing, or any combination of these data transmission types.

The data pump application is fed by the real-time data of the SSCOs andconfigured to report the state of health, application state, transactioninformation, and intervention activity that is fed into plug-in DynamicLinked Libraries (DLLs) for further consumption by retail-specificapplications.

In an embodiment, the data pump application reports key metrics as:availability of SSCOs and RAPs, software component versions, and SSCOfailed transaction rates.

In an embodiment, the data pump application produces report files thatare pushed to an assigned Intranet host within an enterprise using anyfile transfer mechanism.

In an embodiment, the data pump application uses a real-time messagingapplication for reporting in real time the data processed by the datapump application to an enterprise-specific dashboard application.

In an embodiment, the data pump application is fed enterprise'smonitoring and security system and hosted by the RAP or by anindependent server that is independent of the RAP.

Transaction information and status information passes through the SSCOsto the RAP terminal where the data is fed to the data pump applicationas messages generated by the RCMs. Each custom plugin of the data pumpapplication develops an aggregation of the data, which then reports thedata as it is configured to.

In an embodiment, the status reported for the SSCOs is selected from:out-of-service (not running the appropriate software), full service(able to process transactions and accept card and cash payments), cardonly (able to process card only transactions), cash only (able toprocess cash only transactions) lane error (software is running but dueto an error is unable to process transactions due to an error state),lane closed (software is running but is unable to process transactionsbecause the lane is closed), and attendant mode (terminal is using anattendant mode of operation).

The systems 100 and 190 provide a seamless integration into existingmonitoring and security-based systems for providing customized dataaggregation and integration processing within an enterprise. The systems100 and 190 can be internal to the existing systems or accessed asexternal-based services over a WAN connection.

In an embodiment, the system 100 utilizes the remote service 180 and thedata manager 185 in a cloud processing environment.

These and other embodiments are now discussed with reference to theFIGS. 2-4.

FIG. 2 is a diagram of a method 200 for customized data aggregation andintegration processing, according to an example embodiment. The softwaremodule(s) that implements the method 200 is referred to as a “dataintegration manager.” The data integration manager is implemented asexecutable instructions programmed and residing within memory and/or anon-transitory computer-readable (processor-readable) storage medium andexecuted by one or more processors of a device. The processor(s) of thedevice that executes the data integration manager are specificallyconfigured and programmed to process the data integration manager. Thedata integration manager has access to a network during its processing.The network can be wired, wireless, or a combination of wired andwireless.

In an embodiment, the device that executes the data integration manageris the management terminal 160 of the FIG. 1A.

In an embodiment, the device that executes the data integration manageris the remote server 180 of the FIG. 1A.

In an embodiment, the device that executes the data integration manageris the RAP terminal of the FIG. 1B.

In an embodiment, the device that executes the data integration manageris a collection of devices logically organized as a cloud processingenvironment.

In an embodiment, the data integration manager is the data manager 165.

In an embodiment, the data integration manager is the data manager 185.

In an embodiment, the data integration manager is the data pump serverapplication of the FIG. 1B.

At 210, the data integration manager receives, in real time from agents,data collected on a plurality of terminals. In an embodiment, theterminals include some combination of: a SST, a POS terminal, and adigital sign.

According to an embodiment, at 211, the data integration managerreceives the data from the agents that execute on POS terminals; the POSterminal is the terminals.

In an embodiment of 211 and at 212, the data integration managerreceives the data as a plurality of data associated with transactionsprocessing on the POS terminals and associated with the POS terminals.The plurality of data includes: events data, metrics data, health data,terminal intervention data, and sales data.

In an embodiment of 212 and at 213, the data integration managerseparates the plurality of data into data types based on a schema. Theschema maps formats for the plurality of data to the data types thatcorresponds to the events data, the metrics data, the health data, theterminal intervention data, and the sales data.

At 220, the data integration manager selectively aggregates a portion ofthe data into aggregated data.

In an embodiment of 213 and 220, at 221, the data integration managermaps from the schema one of the data types to an aggregation algorithmand provides the data from the plurality of data associated with thatdata type to the aggregation algorithm as input.

In an embodiment of 221, at 222, the data integration manager receivesthe aggregated data as output from the aggregation algorithm.

At 230, the data integration manager translates the aggregated data intoa target data format.

In an embodiment of 222 and 230, at 231, the data integration manageridentifies the targeted data format from the schema.

In an embodiment of 230 and at 232, the data integration manageridentifies the targeted data format based on a resource identifier forthe resource included in a schema.

At 240, the data integration manager determines a time to provide theaggregated data in the targeted data format to a resource based onevaluation of a condition.

In an embodiment, at 241, the data integration manager obtains thecondition from a schema.

In an embodiment, at 242, the data integration manager identifies theresource as a collection of different resources and provides theaggregated data to each of the different resources in the collection ofresources.

In an embodiment, in addition to the aggregated data, the data orselected portions of the data is provided by the data integrationmanager to the resource in the targeted data format.

FIG. 3 is a diagram of another method 300 for customized dataaggregation and integration processing, according to an exampleembodiment. The software module(s) that implements the method 300 isreferred to as a “data integrator.” The data integrator is implementedas executable instructions programmed and residing within memory and/ora non-transitory computer-readable (processor-readable) storage mediumand executed by one or more processors of a hardware device. Thehardware processors that execute the data integrator are specificallyconfigured and programmed to process data integrator. The dataintegrator has access to one or more networks during its processing.Each network can be wired, wireless, or a combination of wired andwireless.

In an embodiment, the device that executes the data integrator is themanagement terminal 160 of the FIG. 1A.

In an embodiment, the device that executes the data integrator is theremote server 180 of the FIG. 1A.

In an embodiment, the device that executes the data integrator is theRAP terminal of the FIG. 1B.

In an embodiment, the device that executes the data integrator is acollection of devices logically organized as a cloud processingenvironment.

In an embodiment, the data integrator is the data manager 165.

In an embodiment, the data integrator is the data manager 185.

In an embodiment, the data integrator is the data pump serverapplication of the FIG. 1B.

In an embodiment, the data integrator is the method 200.

At 310, the data integrator collects data from a plurality oftransaction terminals in real time.

In an embodiment, at 311, the data integrator receives the data fromagents that execute on the transaction terminals.

In an embodiment, the transaction terminals are one of: POS terminals,SST terminals, and/or digital signs.

At 320, the data integrator selectively aggregates portions of the datainto aggregated data.

In an embodiment, at 321, the data integrator provides the portions ofthe data as input to a second plugin that provides the aggregated data.

At 330, the data integrator evaluates a condition to determine when toprovide other portions of the data and the aggregated data to aresource.

In an embodiment, at 331, the data integrator identifies the conditionas a particular condition that indicates the other portions of the dataand the aggregated data is to be provided in real time to the resource.

In an embodiment, at 332, the data integrator identifies the conditionas a time-based condition.

In an embodiment, at 333, the data integrator identifies the conditionas a particular condition that indicates the other portions of the dataand the aggregated data is to be provided to the resource when athreshold value is reached or exceeded for one or more of: an amount ofdata collected and a value associated with the aggregated data.

In an embodiment, at 334, the data integrator identifies the conditionas a user-provided and user-defined condition.

At 340, the data integrator provides the other portions of the data andthe aggregated data to a plugin associated with the resource when thecondition is satisfied.

According to an embodiment, at 350, the data integrator processes on oneof: a remote server, a cloud, and a local management terminal that islocated within a same LAN as the transaction terminals.

FIG. 4 is a diagram of a system for customized data aggregation andintegration processing 400, according to an example embodiment. Somecomponents of the system 400 are programmed and reside within memoryand/or a non-transitory computer-readable medium and execute on one ormore processors of a device or a set of devices. The system 400communicates over one or more networks, which can be wired, wireless, ora combination of wired and wireless.

In an embodiment, the system 400 is the system 100 of the FIG. 1A.

In an embodiment, the system 400 is the system 190 of the FIG. 1B.

In an embodiment, the system 400 performs and implements all or somecombination of the processing discussed above with the FIGS. 1A, 1B, 2,and/or 3.

The system 400 includes at least one hardware processor 401 and a datamanager 402.

The data manager 402 is configured to: 1) execute on the processor 401,2) receive data in real time from a plurality of agents that areexecuting on a plurality of transaction terminals, 3) selectivelyaggregate portions of the data as aggregated data, 4) selectivelydetermine a time for reporting the aggregated data and other portions ofthe data, and 5) provide the aggregated data and the other portions ofthe data to at least one resource at the time.

In an embodiment, the transaction terminals transaction terminalsinclude one or more combinations of: a digital sign, a Self-ServiceTerminal (SST), a mobile device having a Self-Service application forperforming transactions, and a Point-Of-Sale (POS) terminal. In anembodiment, the mobile device can include: a tablet, a laptop, a phone,and a wearable processing device.

In an embodiment, the data manager 402 is one of, all of, or acombination of: the data manager 165, the data manager 185, the datapump server application of the FIG. 1B, the method 200, and/or themethod 300.

It should be appreciated that where software is described in aparticular form (such as a component or module) this is merely to aidunderstanding and is not intended to limit how software that implementsthose functions may be architected or structured. For example, modulesare illustrated as separate modules, but may be implemented ashomogenous code, as individual components, some, but not all of thesemodules may be combined, or the functions may be implemented in softwarestructured in any other convenient manner.

Furthermore, although the software modules are illustrated as executingon one piece of hardware, the software may be distributed over multipleprocessors or in any other convenient manner.

The above description is illustrative, and not restrictive. Many otherembodiments will be apparent to those of skill in the art upon reviewingthe above description. The scope of embodiments should therefore bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

In the foregoing description of the embodiments, various features aregrouped together in a single embodiment for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting that the claimed embodiments have more features than areexpressly recited in each claim. Rather, as the following claimsreflect, inventive subject matter lies in less than all features of asingle disclosed embodiment. Thus the following claims are herebyincorporated into the Description of the Embodiments, with each claimstanding on its own as a separate exemplary embodiment.

1. A method, comprising: receiving, in real time, from a plurality ofagents data collected on a plurality of terminals; selectivelyaggregating a portion of the data into aggregated data; translating theaggregated data into a target data format; and determining a time toprovide the aggregated data in the targeted data format to a resourcebased on an evaluated condition.
 2. The method of claim 1, whereinreceiving further includes receiving the data from the plurality ofagents that execute on Point-Of-Sale (POS) terminals, wherein theterminals are the POS terminals, and wherein the POS terminals includeSelf-Service Checkout terminals.
 3. The method of claim 2, whereinreceiving further includes receiving the data as a plurality of dataassociated with transactions processing on the POS terminals and the POSterminals, wherein the plurality of data includes events data, metricsdata, health data, intervention data, and sales data.
 4. The method ofclaim 3, wherein receiving further includes separating the plurality ofdata into data types based on a schema, the schema mapping formats forthe plurality of data to the data types that correspond to the eventsdata, the metrics data, the health data, the intervention data, and thesales data.
 5. The method of claim 4, wherein selectively aggregatingfurther includes mapping from the schema one of the data types to anaggregation algorithm and providing the data from the plurality of dataassociated with the one data type to the aggregation algorithm as input.6. The method of claim 5, wherein mapping configuring further includesreceiving the aggregated data as output from the aggregation algorithm.7. The method of claim 6, wherein translating further includesidentifying the targeted data format from the schema.
 8. The method ofclaim 1, wherein translating further includes identifying the targeteddata format based on a resource identifier for the resource included ina schema.
 9. The method of claim 1, wherein determining further includesobtaining the condition from a schema.
 10. The method of claim 1,wherein determining further includes identifying the resource as acollection of different resources and providing the aggregated data toeach of the different resources in the collection.
 11. A method,comprising: collecting data from a plurality of transaction terminals inreal time; selectively aggregating portions of the data into aggregateddata; evaluating a condition to determine when to provide other portionsof the data and the aggregated data to a resource; and providing theother portions of the data and the aggregated data to a pluginassociated with the resource when the condition is satisfied.
 12. Themethod of claim 11, wherein collecting further includes receiving thedata from agents that execute on the transaction terminals.
 13. Themethod of claim 11, wherein selectively aggregation further includesproviding the portions of the data as input to a second plugin thatprovides the aggregated data as output.
 14. The method of claim 11,wherein evaluating the condition further includes identifying thecondition as a particular condition that indicates the other portions ofthe data and the aggregated data is to be provided in real time to theresource.
 15. The method of claim 11, wherein evaluating the conditionfurther includes identifying the condition as a time-based condition.16. The method of claim 11, wherein evaluating the condition furtherincludes identifying the condition as a particular condition thatindicates the other portions of the data and the aggregated data is tobe provided to the resource when a threshold value is reached orexceeded for one or more of: an amount of the data collected and a valueassociated with the aggregated data.
 17. The method of claim 11, whereinevaluating the condition further includes identifying the condition as auser-provided and user-defined condition.
 18. The method of claim 11further comprising, processing the method on one of: a remote server, acloud, and a local management terminal that is located within a sameLocal Area Network (LAN) as the transaction terminals.
 19. A system,comprising: at least one processor; a data integrator configured to: i)execute on the processor, ii) receive data in real time from a pluralityof agents that are executing on a plurality of transaction terminals,iii) selectively aggregate portions of the data as aggregated data, iv)selectively determine when to report the aggregated data and otherportions of the data, and v) provide the aggregated data and the otherportions of the data to at least one resource when appropriate to do so.20. The system of claim 19, wherein the transaction terminals includeone or more combinations of: a digital sign, a Self-Service Terminal(SST), a mobile device, and a Point-Of-Sale (POS) terminal.